The increasing need for a quantitative imaging validation tool is fueled by the propagation of perfusion imaging for cancer treatment prognosis and evaluation. Current methods of providing validation for dynamic contrast-enhanced (DCE) imaging and analyses have been limited to simulated data, which do not account for variances in image acquisition and artifacts, and static phantoms, which do not yet monitor dynamic effects and their analyses. To provide an in situ assessment of the imaging and post-processing effects on a perfused system with properties similar to tissue, a dynamic flow phantom, consisting of a vascular network and a capillary exchange system, is proposed. The design offers the physical modeling of distributed blood flow from arterial-sized vessels down to a capillary-like size scale, through which flow resistance can be varied to produce differential perfusion comparable to expected in vivo measurements. This investigation focuses on the development of a reliable, reproducible dynamic phantom that models the liver's regional anatomy and heterogeneous physiological function. The general design for the proposed phantom would consist of three components: a background, tissue equivalent volume, two embedded fluid re-circulating vascular network, and a series of perfused sections, where exchange occurs between the terminal branches of the feeding vessels into tissue compartments exhibiting differential perfusion, and then into a network of draining vessels. Using tissue scaffold techniques, porous regions with known, controlled flow resistance would be fabricated, such that the volumetric flow through porous media mimics the volumetric flow expected in severely diseased, moderately damaged or normal human liver tissue. The volumetric flow rate through each porous component would be measured empirically. The dynamic phantom system would be modeled using computation fluid dynamics to quantitatively estimate the volumetric flow through the phantom. The physical phantom, with a known perfusion, will be used to validate image- extracted perfusion measurements acquired using a given DCE-CT data acquisition protocol and compartment-based DCE-CT algorithms. The availability of such a quality assurance tool may help to validate perfusion imaging protocols across imaging centers. This new technology can enable the validation of imaging biomarkers' sensitivity and specificity across various organs and disease states. Ultimately, this will prolong the lives of patients through better treatment planning, and help reduce the risks associated with high-dose radiation treatment.